Abstract

In this paper we propose a new strategy for combining
the outputs of several alignment systems. Based on the word
boundaries retrieved from a number of individual alignment
systems, the new boundaries are estimated. We investigate
three strategies for this estimation. First, the mean value
of the individual boundaries is taken, second the median
is selected, and third, confidence values of the alignment
systems are considered. We apply the combination strategies
on a word mapping system for historical handwritten
manuscripts. After some preprocessing and normalizing
steps, three differently trained hidden Markov model
based handwriting recognizers are applied to the text lines
in forced alignment mode. As a result, the positions of the
word boundaries are obtained. In in a number of experiments
it is shown that a combination strategy based on the
median outperforms the others and all individual alignment
systems with a word mapping rate of about 95%.